System and Method for Providing Market Data in an Electronic Trading Environment

ABSTRACT

A system and methods are developed for providing market data in an electronic trading environment. One example method includes determining a probability model comprising a probability corresponding to a change in relation to a market data parameter, then, using the probability to generate a compressed bit stream representing the market data parameter, and providing the compressed bit stream to the client terminal.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application is a continuation of U.S. patent application Ser. No.11/095,100, filed Mar. 31, 2005, entitled “System and Method forProviding Market Data in an Electronic Trading Environment,” thecontents of which are fully incorporated by reference.

TECHNICAL FIELD

The present invention is directed to electronic trading. Morespecifically, the present invention is directed towards providing marketdata in an electronic trading environment.

BACKGROUND

Trading methods have evolved from an exclusively manual process to atechnology enabled, electronic platform. With the advent of electronictrading, a user or trader can be in virtually direct contact with themarket, from practically anywhere in the world, performing nearreal-time transactions, and without the need to make personal contactwith a broker.

In particular, traders are connected to an exchange's electronic tradingplatform by way of a communication link and through an applicationprogram interface to facilitate real-time electronic messaging betweenthemselves and the exchange. The electronic trading platform includes atleast one electronic market, which is the heart of the trading systemfor a particular tradeable object and handles matching of bids andoffers placed by the subscribing traders for that tradeable object. Theelectronic messaging includes market information that is sent from theelectronic exchange to the traders. Once the traders receive marketinformation, it may be displayed to them on their trading screens. Uponviewing the information, traders can take certain actions, including theactions of sending buy or sell orders to the electronic exchange,adjusting existing orders, deleting orders, or otherwise managingorders. Traders may also use software tools on their client devices toautomate these and additional actions.

Just as with an open-outcry exchange, an electronic exchange can listany number of tradeable objects. Often times, traders will tradesimultaneously more than one tradeable object, and they may tradesimultaneously tradeable objects that are listed at more than oneexchange. As used herein, the term “tradeable object” refers to anythingthat can be traded with a quantity and/or price. It includes, but is notlimited to, all types of traded events, goods and/or financial products,which can include, for example, stocks, options, bonds, futures,currency, and warrants, as well as funds, derivatives and collections ofthe foregoing, and all types of commodities, such as grains, energy, andmetals. The tradeable object may be “real,” such as products that arelisted by an exchange for trading, or “synthetic,” such as a combinationof real products that is created by the user. A tradeable object couldactually be a combination of other tradeable objects, such as a class oftradeable objects.

Ordinarily, each tradeable object has its own independent electronicmarket, and therefore, its own separate stream of market data, commonlyreferred to as a data feed. A data feed includes market informationcorresponding to a tradeable object, and the content of the data feedtypically depends on the host exchange and on the tradeable object.Market information in a data feed commonly includes information relatedto the inside market and market depth. The inside market represents thelowest sell price (also referred to as the best or lowest ask price),and the highest buy price (also, referred to as the best or highest bidprice). Then, market depth includes quantities available for trading thetradeable object at certain buy and sell price levels away from theinside market. The extent of market depth available for a trader usuallydepends on the exchange. For example, some exchanges provide marketdepth for an infinite number of price levels, while some provide onlyquantities associated with the inside market, and others may provide nomarket depth at all. Additionally, exchanges can offer other types ofmarket information in the data feeds, such as the last traded price(LTP), the last traded quantity (LTQ), and order fill information.

Since each tradeable object is actually associated with its own separatestream of market information, in the instances when a trader trades morethan one tradeable object, the trader will receive more than one datafeed. In addition to receiving market information from exchanges,traders might subscribe to news feeds, real-time quotation vendors thatprovide information to traders for decision support, and otherinformation sources.

Using client devices, such as a personal computer, laptop computer,hand-held computer, or other devices that have network access, a tradercan link to host exchanges through one or more networks. A network is agroup of two or more computers or devices linked together, andcharacterized by topology, protocol, and architecture. For example, sometraders may link to the host through a direct network connection, suchas a T1 or ISDN. Others may link to the host exchange through directnetwork connections, and through other common network components, suchas high speed servers, routers, and gateways. The Internet, a well-knowncollection of networks and gateways, can be used to establish aconnection between the client device and the host exchange. There aremany different types of wired and wireless networks and combinations ofnetwork types known in the art that can link traders to the hostexchange.

Many, if not all, networks being used by electronic exchanges havelimited bandwidth capacity. Since adding network bandwidth is veryexpensive, many existing exchanges already limit the extent of marketdata being provided to their clients. Also, as the trading applicationsbecome more and more sophisticated, traders may wish to receive morecontent rich market data. Thus, it would be beneficial to provide atrading system that can meet current and future bandwidth needs.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments are described herein with reference to the followingdrawings, in which:

FIG. 1 is a block diagram illustrating an example network configurationthat can be used to access one or more electronic exchanges;

FIG. 2 is a block diagram illustrating an example system that can beused for market data compression according to one example embodiment;

FIG. 3 is a block diagram illustrating an example probability model thatcan be defined in relation to a tradeable object;

FIG. 4 is a block diagram illustrating two market depth snapshotscorresponding to a tradeable object at two consecutive times;

FIG. 5 is a message flow diagram illustrating an example method forproviding market data to a client entity using data encoding based onprobability models;

FIG. 6 is a block diagram illustrating an updated probability model ofFIG. 4 based on market data changes illustrated in FIG. 5;

FIG. 7 is a flowchart illustrating an example method for encoding marketdata according to one example embodiment; and

FIG. 8 is a flowchart illustrating an example method for decoding marketdata according to one example embodiment.

DETAILED DESCRIPTION I. Market Data Compression Overview

The example embodiments, among other things, are directed to providingmarket data to client devices. Typical market data consists of acollection of prices and quantities corresponding to a tradeable object.In general, for any given tradeable object, the market at any instant islikely to be quite similar to the market in the previous instant, withthe change being often zero. If the market moves, it often changes by+/−1 tick. These, as well as other market characteristics, are used inthe example methods and systems described herein to develop statisticalprobability models for use in relation to statistical compression ofmarket data.

More specifically, according to one example system, a sending entity,such as an electronic exchange, or yet some other entity incommunication with the exchange, may include a probability modeler thatdynamically develops a probability model for a tradeable object. Forexample, the probability model can be developed and dynamically updatedbased on market data being encoded and sent to a receiving entity, suchas a client entity, over the network from the electronic exchange. Morespecifically, the probability model for a tradeable object may include aplurality of probability values corresponding to many different marketdata parameters. For example, the probability model may include aprobability for detecting a change of +1 tick in relation to the bestbid price in the market corresponding to the tradeable object. Accordingto one example embodiment, the probability values in the modelcorresponding to the change in the best bid price may be determinedbased on how many times a change of +1 tick has been encoded in relationto the best bid price during a certain time period in the past. Thestatistical compressions methods can then use the developed probabilitymodel to generate a compressed bit stream representing the change inbest bid price, as well as other market data changes, corresponding tothe tradeable object. The compressed bit stream may then be sent overone or more networks to the client entity.

According to one example embodiment, a receiving entity also includesprobability models that are used to decode received compressed bitstreams. In such an embodiment, the probability model that was used atthe time of encoding a change in a certain market parameter is the sameas the one that is used at the receiving entity at the time of decodingthe change. To keep the two probability models synchronized, the modelscan be updated after encoding and decoding the change corresponding tothe market parameter. More details related to market data compressionusing probability models will be described below.

While the example embodiments are described herein with reference toillustrative embodiments for particular applications, it should beunderstood that the example embodiments are not limited thereto. Othersystems, methods, and advantages of the present embodiments will be orbecome apparent to one with skill in the art upon examination of thefollowing drawings and description. It is intended that all suchadditional systems, methods, features, and advantages be within thescope of the present invention, and be protected by the accompanyingclaims.

II. Hardware and Software Overview

As will be appreciated by one of ordinary skill in the art, the exampleembodiments may be operated in an entirely software embodiment, in anentirely hardware embodiment, or in a combination thereof. However, forsake of illustration, the example embodiments are described in asoftware-based embodiment, which is executed on a computer device. Assuch, the example embodiments take the form of a computer programproduct that is stored on a computer readable storage medium and isexecuted by a suitable instruction system in the computer device. Anysuitable computer readable medium may be utilized including hard disks,CD-ROMs, optical storage devices, or magnetic storage devices, forexample.

In an electronic trading environment, when an authorized trader selectsa tradeable object, the trader may access market data related to theselected tradeable object(s). Referring to FIG. 1, an examplecommunication that might occur between an electronic exchange and aclient entity is shown. During a trading session, market data 108, inthe form of messages, may be relayed from a host exchange 106 overcommunication links 116 and 112 to a client entity generally indicatedas 102. The client entity 102 may be a single client terminal that isused by a single trader or multiple client terminals corresponding tomultiple traders associated with one or more trading groups. The cliententity 102 may include any computer that accesses one or more networks.For example, the client entity 102 can be a personal computer, a laptopcomputer, a hand-held device, and so on.

As illustrated in FIG. 1, intermediate devices, such as gateway(s) 104,may be used to facilitate communications between the client entity 102and the host exchange 106. It should be understood that while FIG. 1illustrates the client entity 102 communicating with a single hostexchange 106, in an alternative embodiment, the client entity 102 couldestablish trading sessions to more than one host exchange. Also, itshould be understood that information being communicated to and from theclient entity 102 and the exchange 106 could be communicated via asingle communication path.

The market data 108 contains information that characterizes thetradeable objects, including, among other parameters, order relatedparameters, such as price and quantity, and the inside market, whichrepresents the lowest sell price (also referred to as the best or lowestask price), and the highest buy price (also referred to as the best orhighest bid price). In some electronic markets, market data may alsoinclude market depth, which generally refers to quantities available fortrading the tradeable object at certain buy price levels and quantitiesavailable for trading the tradeable object at certain sell price levels.The methods for providing market data according to the exampleembodiments will be described in greater detail below.

In addition to providing the tradeable object's order book information,electronic exchanges can offer different types of market informationsuch as total traded quantity for each price level, opening price, lasttraded price, last traded quantity, closing price, or order fillinformation. It should be understood that market information providedfrom an electronic exchange could include more or fewer items dependingon the type of tradeable object or the type of exchange. Also, it shouldbe understood that the messages provided in the market data 108 may varyin size depending on the content carried by them, and the software atthe receiving end may be programmed to understand the messages and toact out certain operations.

A trader may view the information provided from an exchange via one ormore specialized trading screens created by software running on theclient entity 102. Upon viewing the market information or a portionthereof, a trader may wish to take actions, such as send orders to anexchange, cancel orders at the exchange, or change order parameters, forexample. To do that, the trader may input various commands or signalsinto the client entity 102. Upon receiving one or more commands orsignals from the trader, the client entity 102 may generate messagesthat reflect the actions taken, generally shown at 110. It should beunderstood that different types of messages or order types can besubmitted to the host exchange 106, all of which may be consideredvarious types of transaction information. Once generated, user actionmessages 110 may be sent from the client entity 102 to the host exchangeover communication links 114 and 116.

The client entity 102 may use software that creates specializedinteractive trading screens on the client entity 102. The tradingscreens enable traders to enter and execute orders, obtain marketquotes, and monitor positions. The range and quality of featuresavailable to the trader on his or her screens varies according to thespecific software application being run. In addition to or in place ofthe interactive trading screens, the client entity 102 may run automatednon-interactive types of trading applications.

A commercially available trading application that allows a user to tradein systems like those shown in FIG. 1 and subsequent figures isX_TRADER® from Trading Technologies International, Inc. of Chicago, Ill.X_TRADER° also provides an electronic trading interface, referred to asMD Trader™, in which working orders and bid/ask quantities are displayedin association with a static price axis or scale. As mentioned above,the scope of the example embodiments described herein are not limited bythe type of terminal or device used, and are not limited to anyparticular type of trading application. Portions of the X_TRADER® andthe MD Trader™-style display are described in U.S. Pat. No. 6,772,132entitled “Click Based Trading With Intuitive Grid Display of MarketDepth,” filed on Jun. 9, 2000 as U.S. patent application Ser. No.09/590,692 and issued Apr. 20, 2004; U.S. patent application Ser. No.09/971,087, entitled “Click Based Trading With Intuitive Grid Display ofMarket Depth and Price Consolidation,” filed on Oct. 5, 2001 and whichissued on Oct. 24, 2006 as U.S. Pat. No. 7,127,424; and U.S. patentapplication Ser. No. 10/125,894, entitled “Trading Tools for ElectronicTrading,” filed on Apr. 19, 2002 and which issued on Jun. 17, 2008 asU.S. Pat. No. 7,389,268, the contents of each are incorporated herein byreference.

A. Market Data Compression System Architecture

FIG. 2 is a block diagram illustrating an example system 200 that can beused for market data compression according to one example embodiment.The example system 200 illustrates a client entity 202 and an electronicexchange 204 communicating via a network 222. According to the exampleembodiments that will be described in greater detail below, theelectronic exchange 204 uses statistical compression techniques tocompress market data that is provided to the client entity 202. WhileFIG. 2 shows only certain elements of the exchange 204 and client entity202 that can be used in relation to data compression/decompression, itshould be understood that a typical exchange and a client entity mayinclude many additional elements, such as interfaces, processors, andapplications, that can be used for receiving and processing data beingcommunicated between the exchange 204 and the client entity 202. Also,it should be understood that while FIG. 2 illustrates a single network222 that is used for sending data between the client entity 202 and theelectronic exchange 204, the two entities could communicate via multiplenetworks and additional intermediate devices, such as gateways, routers,or yet some other network devices. Additionally, while the compressionelements are illustrated as being part of the exchange 204,alternatively, these elements could be located on some other networkentity in communication with the electronic exchange 204.

The electronic exchange 204 includes a compressor system 206. Thecompressor system in turn includes a controller 208, a probabilitymodeler 210, and an encoder 214. The client entity 102 includes adecompressor system 216 with a controller 218, a probability modeler220, and a decoder 224. The compressor system 206 can use one or morecompression algorithms that are to compress market data usingstatistical probability data according to the embodiments that will bedescribed in greater detail below. The decompressor system 216 can useone or more decompression algorithms along with the statisticalprobability data to decompress any compressed data received from theelectronic exchange 202. The principals of statistical data encoding anddecoding are well known in the art, and more details related to theseprocesses can be found, for example, in the publication “ArithmeticCoding Revealed,” by Eric Bodden, et al., fully incorporated herein byreference. The relevant theorem being relied on herein is that theminimum number of compressed bits that can be used to represent a symbolis “−log₂(p(S)),” where “S” is the symbol/parameter to be encoded, and“p” is the probability predicted for the symbol/parameter. The symbols,as used hereinafter, can refer to any parameter related to market data,such as a symbol being used to identify a tradeable object, a marketlevel, a price level, or yet a different parameter associated withmarket data.

As an example, using “−log₂(p(S))” to determine a minimum number of bitsthat can be used to represent a symbol, if the probability of occurrenceof a symbol is 0.25, the symbol could be represented with −log₂(0.25)bits, or 2 bits. It should be understood that the number of bits thatcan be used to represent a symbol does not necessarily have to berepresented with integer numbers. Alternatively, the number of bitscould be represented with decimal numbers as well. For example, if asymbol is predicted with probability of 0.9, it would be representedwith −log₂(0.9) bits, or approximately 0.152 bits. In relation toencoding market data parameters, such as encoding changes in the insidemarket prices or quantities, the above equation would take a format of−log₂ (Tradeable Object Parameter Probabability).

The probability modelers 210 and 220 generate market data probabilitymodels 212 and 222. According to the example embodiment shown in FIG. 2,the compressor system 206 uses the probability models 212 to compressand encode market data and generate a compressed data stream to be sentto the client entity 202. Similarly, the probability models 222 can beused at the client entity 202 to decode and decompress receivedcompressed data stream. The probability models include statistical datarepresenting the probabilities of detecting a change in relation tocertain parameters associated with market data, the examples of whichwill be described in relation to subsequent figures.

The probability models could be dynamic or static. The exampleembodiments hereinafter describe probability models that are dynamicallygenerated and updated during a trading day based on a frequency orhistory of changes in relation to certain parameters that the modelerhas already seen. For example, the probability model for symbols A, B,and C that has seen the sequence of symbols ABACAABC, where, forexample, the symbols correspond to changes detected in relation to threemarket levels, the probability modelers 210 and 220 could predict thatthe probability that the next symbol will be A is 4/8 or 0.5, and B andC would be predicted with the probability of 2/8 or 0.25. Sometimes, itmay be known that certain symbols are not possible in a given context.In such cases, these symbols could be removed from consideration as thenext possible symbol. For example, referring to the above example, if itis known that A could not be the next symbol, B and C would each bepredicted with the probability of 2/4 or 0.5. The process of updatingthe probability models 210 and 222 could be performed by controllers 208and 218, respectively.

According to one example embodiment, each tradeable object could beassociated with its own probability model. FIG. 2 shows a plurality ofprobability models created for a plurality of tradeable objects, “TO 1”through “TO X.” Each tradeable object probability model may in turninclude a number of probability sub-models. A probability sub-model maydefine probability levels for one or more market data parametersassociated with the tradeable object. As mentioned earlier, typicalmarket data that will be compressed consists of a collection of pricesand quantities corresponding to a tradeable object. For each tradeableobject, there are two sides of market depth, a bid side and an ask side,along with a variety of different parameters, such as a last tradedprice and a last traded quantity. The market depth, as mentionedearlier, may include a preset number of levels ranging from only theinside market to an unlimited number of levels. While the number ofmarket depth levels that will be compressed may be equal to the numberof market depth levels typically provided by the exchange, fewer marketdepth levels could be compressed as well.

FIG. 3 is a block diagram illustrating an example probability model 300that can be defined in relation to a tradeable object. The probabilitymodel 300 includes a “Parameter” field 302, a “Frequency” field 304, a“Probability” field 306, and a “Number of Bits” field 308. When sendingmarket data, the probability models can be used to predict the tradeableobject that has changed. As mentioned earlier, to ensure that a changein a tradeable object can be represented as a bit sequence, everytradeable object symbol is assigned at least some non-zero probability.

As shown in FIG. 3, the first sub-model is a “Tradeable Object Symbol”model 310 that can be used to encode a symbol associated with a specifictradeable object. The sub-model 310 includes five tradeable objects,ES-Mar05, ES-Jun05, NQ-Mar05, NQ-Jun05, and “Other” to be used inrelation to other tradeable objects that are not specifically defined inthe model 310. It should be understood that the models are not limitedto any specific tradeable objects and could be defined in relation tofewer, more, or different tradeable objects than those shown in FIG. 3.Also, any parameters described in relation to the model 300 should notbe viewed as limiting, but as examples only.

The “Frequency” field 304 specifies the number of times a change hasbeen detected in relation to a specific tradeable object during apredetermined time period. It should be understood that any time periodcould be used, such as one or more trading days starting from the timewhen the markets open, or some other user-defined time interval. Thefrequency data in the probability models are preferably updated suchthat at the time of encoding certain market data parameter the frequencyvalues at both ends of the network are the same. More details related toupdating the probability models will be described below. According toone example embodiment, the probability modelers 208 and 216 couldmonitor changes in market data, and then could update the numbers in thefrequency field 304 every time a certain symbol associated with atradeable object is encoded/decoded. To improve accuracy of frequencydata, the probability modelers 208 and 216 could weigh recent data moreheavily than older data. To do that, the modelers could consider only afixed number of previous encodings in making its predictions.Alternatively, exponential weighting methods could be used to decreaseweigh for less recently encoded parameters. Also, rather than startingfrequency models from no data at all, the frequency values could beinitiated using historical data so that the probability models can beused to make reasonable predictions before being dynamically encoded asufficient number of times to build the most current probability models.It should be understood that those skilled in the art will understandthat different methods could also be used to determine/update theFrequency values.

The sub-models 312-326 correspond to the tradeable object ES-Mar05.According to one example embodiment, each tradeable object listed in theTradeable Object Symbol sub-model 310 would be associated with its ownprobability models, such as those described below. Alternatively,certain tradeable objects could be highly correlated with each other,such as, for example, options on the same tradeable object with similarstrike prices and expiration dates. In such an embodiment, a singleprobability model with a plurality of sub-models could be used toencode/decode more than one tradeable object. Those skilled in the artwill appreciate that different variations of grouping and developing theprobability models described herein are possible as well.

Referring to the probability model 300, the frequency of detecting achange in relation to the tradeable object having the symbol ES-Mar05 is20, as specified in the Frequency column 304. Based on that frequencyvalue, the probability of detecting a change in relation to market dataassociated with the tradeable object is 0.487805, as specified in theProbability column 306. According to the example embodiment in FIG. 3,the probability of 0.487805 for the ES-Mar05 is determined by dividingthe frequency corresponding to the symbol (in this example, 20) by thetotal number of frequencies corresponding to all tradeable objects inthe model (in this example, 41). The number of bits to encode the symbolES-Mar05 is 1.035624, as defined in the “Number of Bits” column 308.According to the example embodiments presented herein, the number ofbits can be determined using the formula referenced above,“−log₂(p(S)),” which in this example corresponds to −log₂(0.487805), or1.035624 bits. It should be understood that the number of bits could berounded to a specific number of decimal places predefined to be used inrelation to the number of bits and/or probability values.

The example embodiments described herein in reference to encoding anddecoding price levels and quantities use the incremental approach torepresent a change in the market. In general, for any given tradeableobject, the market at any given instant is likely to be quite similar tothe market in the previous instant. Often, this number is zero. If it isnot zero, it is often either +1 or −1, or some other number. While theprobability sub-models described below use such incremental approach, itshould be understood that different methods to represent a change orspecific values could be used as well.

The probability model 300 further includes the “Best Bid Change”probability sub-model 312, which can be used to encode the best bidprice movement. According to the “Best Bid Change” sub-model 310, forexample, the frequency of the best bid being one tick lower than theprevious one is 3, and the number of bits that will be used to encodesuch a change is 2.807355. The frequencies of the best bid being at thesame market level, at one tick higher, or yet at some other level arealso provided in relation to the “Best Bid Change” sub-model 312, andcorrespond to the frequencies of 15, 12, and 1, respectively. Theprobability values and the corresponding number of bits to be used toencode and decode each change are shown in columns 306 and 308.

In addition to encoding data related to price levels, such as the bestbid price, quantity related data associated with a plurality of pricelevels can be encoded as well. According to one example embodiment, theprocess of encoding prices and quantities can be continued untilencoding of a preset number of non-zero market depth levels is complete.One example quantity encoding/decoding sub-model is illustrated inrelation to the “First Qty Change” sub-model 314. The sub-model 314shows five example values representing a quantity change, including achange by −13, −10, 0, 10, and “Other.” For example, if there has beenno change in the quantity, which corresponds to the “0” change, thisinformation will be represented using 0.807355 bits. It should beunderstood that the quantity change values being used in the “First QtyChange” sub-model 314 can be determined by the probability modelers 208and 216 based on market activity or prior encodings.

According to one example embodiment, quantities at price levels otherthan the best bid price can be encoded and decoded using two probabilitysub-models. The first sub-model can be used to encode and decode whetherthe quantity at that price level is zero. Then, only if there is pendingquantity at the price level, the second sub-model can be used to encodethe difference between the new quantity and the last known quantity.This specific implementation is shown in relation to the sub-models 316and 318. The first sub-model 316 is used to encode the informationwhether the quantity at the second price level (the price levelfollowing the best bid) is zero. Then, the sub-model 318 can be used toencode the relative level of the current quantity as compared to thepreviously known quantity at the same price level. It should beunderstood that negative quantity change values in the probabilitysub-models that would lead to non-positive quantity can be factored outof the sub-model before encoding any quantity changes.

It should be understood that while the probability model 300 in FIG. 3illustrates only one sub-model corresponding to a quantity at the pricelevel below the best bid price, additional sub-models could be createdas well for additional price levels. Also, while FIG. 3 only showssub-models corresponding to the bid side of the market, similarsub-models would be developed for prices and quantities corresponding tothe ask side of the market.

In addition to encoding prices and corresponding quantities, theprobability sub-models could also be used to encode other parametersthat are typically provided in relation to market data. The probabilitymodel 300 shows one such example in relation to the last traded priceand the corresponding last traded quantity. The Last Price Changesub-model 320 can be used to encode whether the last traded price isdifferent from the previously provided last traded price. For example,as shown in relation to the sub-model 320, the probability of the lasttraded price being different than the previous traded price is 0.45, andsuch information could be encoded using 1.152003 bits. Then, if the lasttraded price is different, the type of the current last traded pricecould be encoded using the probability sub-model “Last Price Type” 322.The “Last Price Type” sub-model 322 defines a few types, including, aprevious bid (“Prey Bid”), a previous ask (“Prey Ask”), “Current Bid,”“Current Ask,” and “Other.” While the probability sub-model 322 showsfive types that can be used in relation to the last traded price,different definitions could be used as well. Using the probabilitysub-model 322, if the current last traded price corresponds to theprevious bid, this information can be encoded using 1.169925 bits. Then,the change in the last traded price can be encoded using the “Last PriceChange” sub-model 324. If the last traded price changes by +1 tick,then, such a change can be encoded using 1 bit. It should be understoodthat similar probability sub-models could be defined for differentmarket related parameters as well, and the example embodiments are notlimited to the last traded price only. The quantity corresponding to thelast traded quantity can be encoded using the “Last Qty” sub-model 326.For example, if the last traded quantity is 10, that value will beencoded using 0.847997 bits. It should be understood that the quantityvalues illustrated in relation to the sub-model 326 are only examples,and different values could also be specified in the model based on thehistorical values corresponding to the last traded quantities.

FIG. 4 is a block diagram 400 illustrating two market depth snapshots402 and 404 corresponding to a tradeable object at two consecutivetimes, T1 and T2. The two market snapshots include six price levels,ranging from 1175.00 to 1173.75, and the corresponding quantity values.As shown in relation to the market depth 402 corresponding to time T1,the best bid having the quantity of 10 is at the price level of 1174.25,while the best ask corresponds to the price level of 1174.75 and has thequantity of 25. The market depth snapshot 402 also indicates that thelast traded quantity of 20 was traded at the price level of 1174.75. Themarket depth snapshot 404 illustrates market depth corresponding to thesame tradeable object at time T2. According to the snapshot 404, themarket has moved, resulting in the best bid quantity of 10 being now atthe price level of 1174.00, and a new available quantity of 10 being at1173.75. Also, as indicated in relation to the market snapshot 404, thelast traded quantity of 10 was traded at the price level of 1174.25. Themarket data illustrated in FIG. 4 will be used hereinafter to illustratemethods for encoding market data using probability models described inFIG. 3, and updating the probability models based on the changes in themarket data to generate updated probability models.

FIG. 5 is a message flow diagram 500 illustrating a method for providingmarket data to a client entity using data encoding based on probabilitymodels. The message flow diagram 500 will be described in relation tothe entities illustrated in FIG. 2. However, it should be understoodthat the messages could be used in relation to different networkentities as well. Also, while FIG. 5 illustrates a sequence of messages,the example embodiments are not limited to this specific messagesequence, and the same operations could be accomplished using more orfewer messages that can be sent in a different order than that shown inFIG. 5.

The message flow diagram 500 will be used to illustrate the process ofencoding/decoding the change in the best bid price corresponding to thetradeable object based on the market depth data illustrated in FIG. 4.Also, it will be assumed that the market depth data illustrated in FIG.4 corresponds to the ES Mar05 having the current probability modelillustrated in FIG. 3.

Referring to FIG. 5, when the compressor system 206 detects a request toencode a change in the best bid price, such as, in this example, achange from 1174.25 to 1174.00, the compressor controller 208 may querythe probability modeler 210 to get probability data to be used inrelation to encoding the best bid price associated with ES Mar05.According to one example embodiment, the compressor controller 208 maysend a request to the probability modeler 210, such as a “Get SymbolProb” request 502, illustrated in FIG. 5, to get the probability datafor the best bid price corresponding to the ES Mar05. The probabilitymodeler 210 may then look up the requested data in the probabilitymodels 212, and provide the requested data to the compressor controller208, as shown in “Symbol Prob” 504. The “Get Symbol Prob” request 502may include an identifier being used for encoding the best bid pricecorresponding to the ES Mar05. The “Symbol Prob” 504 may in turn includethe probability value to be used for encoding the change in the best bidprice. If the probability value is provided, the encoder 214 may use itto determine the number of bits to be used to encode the change in thebest bid price. Alternatively, the “Symbol Prob” 504 could define thenumber of bits to be used to encode the change in the best bid price.

Referring back to the example market depth data in FIG. 4 and theprobability model in FIG. 3, the probability of the best bid changing by−1 tick is 0.142857, and the number of bits that can be used to encodethis change is 2.807355. According to the example embodiment, the“Symbol Prob” 504 can include the probability value, the number of bits,or both values. Referring back to FIG. 5, the compressor controller 208may then send a request to the encoder 214 to encode the best bidchange, as shown with “Encode (Symbol, Symbol Prob)” 506. The request506 may include an indicator of the symbol to be encoded, here the bestbid, and the symbol probability data, in this example, either theprobability value, the number of bits to be used to encode the change,or both.

The encoder 214 may then encode the change in the best bid price usingthe provided probability data. According to the example probabilitysub-model 312 in FIG. 3, the change in the best bid by −1 tick would beencoded using 2.807355 bits. The encoder 214 may then provide theencoded best bid change data back to the compressor controller 208. Thecompressor controller 208 may then update the probability sub-modelcorresponding to the best bid price. This update is illustrated in“Adapt (Symbol)” request 510. More specifically, according to oneexample embodiment used in relation to the probability models describedherein, the frequency value corresponding to −1 tick change in the bestbid change probability sub-model will be increased by 1 to the frequencyof 4, and the values for the probabilities as well as the number of bitsto be used to compress the best bid changes will be recalculatedaccordingly. The updated best bid change probability sub-model isillustrated in relation to FIG. 6 at 612, more details of which will bedescribed in greater detail below.

The compressor 206 then sends the compressed and encoded datacorresponding to the best bid price change to the decompressor system216, as shown at 512, and the data 512 is received at the decompressorcontroller 218. It should be understood that, in another embodiment, theencoder 214 could first encode all changes in the market correspondingto a tradeable object before sending any data; however, differentembodiments are possible as well. When the decompressor controller 218receives the compressed and encoded data bits corresponding to the bestbid price change, it gets the probability data corresponding to the bestbid change from the probability modeler 220, as shown at 514 to decodethe received data. According to the example embodiment, the probabilitydata at the probability modeler 220 is the same as the probability datathat was used to encode the best bid change at the compressor side ofthe system before updating the corresponding probability sub-model.Using the same probability data, the decoder 224 can correctly decodethe received data. When the decompressor controller 218 receives theprobability data, as shown at 516, it can send the received data and theprobability data to the decoder 224. Upon decoding the data, the decoder224 may provide the decoded bits to the decompressor controller 218.Upon decoding the received data, the probability sub-model correspondingto the best bid price change can be updated so that it matches thecurrent best bid price change sub-model at the compressor side of themarket, as shown at 522. In the example provided herein, the updatedbest bid probability sub-model on the decompressor side will correspondto the sub-model 612 illustrated in FIG. 6.

Once the received data is decoded and decompressed, the decompressorcontroller 218 can provide the data to a trading application for displayat the client entity. The data could be provided to differentapplications as well. It should be understood that the same or similarmethods could be used to encode/decode other changes in the market.Also, it should be understood that while FIG. 5 illustrates individualmessages being used in relation to a single market data parameter, in analternative embodiment, a single message can be used to conveyinformation associated with a plurality of market data parameters. Forexample, the “Get Symbol Prob” message 502 can be used to query theprobability modeler 210 for probability data corresponding to more thanone market data parameter, such as market data parameters other thancorresponding to the best bid price, in this example. Similarly, the“Encode” message 506 could include a request to encode more than onemarket data parameter.

FIG. 6 is a block diagram illustrating an updated probability model ofFIG. 4 based on market data illustrated in FIG. 5. The probability model600 shows the probability sub-models discussed in relation to FIG. 3,and updated based on the market changes illustrated in FIG. 4. Inaddition to modification of the “Best Bid Change” sub-model 612 based ona change in the best bid price by −1 tick, other sub-models are updatedaccordingly. The “First Qty Change” sub-model 614 has been updated basedon the best bid quantity being the same as the previous best bidquantity. As shown in relation to the “First Qty Change” sub-model 614,the frequency value corresponding to “0” quantity change is updated from12 to 13. Then, the probability values and the number of bits shown incolumns 606 and 608 are recomputed accordingly for each best bid changevalue. Also, while not shown in FIG. 6, since the current quantity valueis 10, the quantity changes corresponding to −13 and −10 could beexcluded from the “First Qty Change” sub-model 614 since they would leadto a negative quantity or no quantity at all at the best bid price inrelation to the next quantity value of the corresponding bid price. Insuch an embodiment, the probability values corresponding to “0,” “10,”and “Other” changes would be recomputed accordingly.

Referring next to the “Is 2^(nd) Zero” probability sub-model 616, sincethe quantity available at the next to the best available bid price, herethe price level of 1173.75, is not zero, the frequency valuecorresponding to the “No” parameter is updated from 20 to 21. Also,since the quantity at the price level of 1173.75 has increased from 0 to10, the frequency value corresponding to the change of 10 in the “2^(nd)Qty Change” probability sub-model 618 is increased from 1 to 2, and theprobabilities as well as the values corresponding to the number of bitsto be used for encoding data are recalculated accordingly.

FIG. 4 also shows a change in the last traded price and the last tradedquantity, with the last traded price changing from 1174.75 to 1174.25,and the last traded quantity changing from 20 to 10. The change in thelast traded price is used to update the “Last Price Change” probabilitysub-model 620. Also, since the last traded price corresponds to theprevious bid price, the “Last Price Type” sub-model 622 is updated byincreasing the frequency value corresponding to the “Prey Bid” from 4 to5. Also, since the last traded price corresponds to one of the pricetypes listed in the “Last Price Type” sub-model 622, no changes areshown in relation to the “Last Price Change” sub-model 624. Finally,since the last traded quantity is 10, the frequency corresponding to thevalue of “10” in the “Last Qty” sub-model 626 has been updated from 5 to6.

FIG. 7 is a flowchart 700 illustrating an example method 700 forencoding market data. It should be understood that each block in thisand subsequent flowcharts may represent a module, segment, or portion ofcode, which includes one or more executable instructions forimplementing specific logical functions or steps in the process.Alternate implementations are included within the scope of the exampleembodiments in which functions may be executed out of order from thatshown or discussed, including substantially concurrently or in reverseorder, depending on the functionality involved, as would be understoodby those reasonably skilled in the art of the present invention. Theflowchart 700 will be described in relation to the componentsillustrated in FIG. 2. However, it should be understood that more,fewer, or different components could also be used to execute the method700.

Referring to FIG. 7, at step 702, the encoder 214 encodes a symbolcorresponding to a tradeable object with respect to which a change inmarket data has been detected. It should be understood that each step ofencoding in FIG. 7 involves using methods and probability modelsdiscussed in relation to the preceding figures. At step 704, the encoder214 encodes a change in the best bid price corresponding to thetradeable object. As explained in relation to preceding figures,encoding a change in the best bid price may include encoding that therehas been no change in the best bid price. At step 706, the encoder 214encodes a quantity change corresponding to the best bid. Then, at step708, the encoder moves to the next price level on the bid side of themarket corresponding to the tradeable object. At step 710, the encoder214 determines if the quantity at the next price level is zero. Then, ifthe quantity at the next price level is zero, at step 712, the encoder214 encodes it as “True,” and moves to the next price level on the bidside of the market, as shown at 708. Referring back to step 710, if thequantity at that price level is not zero, the encoder 214 encodes it as“False,” as shown at 714. Then, since the quantity is not zero, at 716,the encoder 214 can encode the quantity change.

According to one example embodiment, the exchange 204 may provide acertain number of market depth levels, and, based on that number, theencoder 214 may be programmed to encode and provide a certain number ofmarket depth levels to the client entity 202. It should be understoodthat the number of market depth levels to be encoded at the encoder 214could be the same or lower than that being normally provided from theexchange 204. Also, the number of market depth levels to be encoded bythe encoder 214 may be determined by the number of non-zero market depthlevel, e.g., the market depth levels having non-zero quantity values. Atstep 718, the encoder 214 determines if enough non-zero market depthlevels have been already encoded. If not, the method 700 continues atstep 708. Otherwise, as shown at 720, the encoder 214 proceeds toencoding the ask side of the market corresponding to the tradeableobject. According to one example embodiment, the encoder 214 couldencode the ask side of the market by following the steps that were usedto encode the bid side of the market, which in this example correspondto steps 704-718.

When the encoder 214 encodes the market depth information, it can alsoencode other market related parameters. The method 700 illustrates theprocess that can used to encode the last traded price and the lasttraded quantity. However, it should be understood that differentparameters could be encoded using the same or similar methods. At step722, the encoder 214 determines if data related to the last trade haschanged. If no changes in the last traded quantity or price have beendetected the method 700 terminates. Otherwise, at step 724, the encoder214 encodes the type of the last traded price. The type of the lasttraded price could be a last bid, a last ask, or yet different types,the examples of which were defined earlier. Then, at step 726, theencoder 214 determines if the last price type was encoder as “Other.” Ifso, at 728, the encoder 214 will encode the change in the last tradedprice. At step 730, the encoder 214 may encode the last traded quantity,and the method 700 terminates. As mentioned earlier, the method 700could continue if the encoder 214 is programmed to encode additionalmarket data related parameters.

FIG. 8 is a flowchart illustrating an example method 800 for decodingmarket data.

When the encoded market data is received at the decoder 224, at step802, it may first decode the symbol corresponding to a tradeable objectassociated with the received data. Similarly to the method 700, itshould be understood that each decoding step described in relation tothe method 800 may involve using the probability models and methodsdescribed above. At step 804, the decoder 224 may decode a change in thebest bid price. At steps 804 and 806, the decoder 224 decodes a changein the best bid price and the best bid quantity. At step 808, thedecoder 224 may move to decoding the next price level. At step 810, thedecoder 224 decodes the bit sequence defining if the next price level iszero. At step 812, the decoder 224 determines if the next price level iszero. If it is, at step 814, it sets the quantity level at that pricelevel to zero, and the method 800 continues at step 808. Referring backto step 812, if the price level is not zero, at step 816, the decoder224 decodes a quantity change corresponding to that price level. At step818, the decoder 224 determines if it has decoded enough non-zero pricelevels. It should be understood that this number can be preconfigured onthe decoder, or it could be provided in relation to the encoded datareceived from the exchange. If not enough non-zero price levels havebeen decoded, the method 800 continues at step 808. Otherwise the stepsdescribed above in relation to decoding the bid side of the market arerepeated for the ask side of the market, as shown at step 820.

Once the decoder 224 decodes market data corresponding to the bid andask side of the market, the decoder 224 may proceed to decoding othermarket related parameters. Decoding of such parameters will be describedin relation to decoding data related to the last trade; however,different parameters could be decoded as well. At step 822, the decoder224 decodes data defining if the last trade has changed. At step 824,based on the decoded data, the decoder 224 determines if the last tradehas changed. If the last trade has not changed, the method 800terminates. Otherwise, at step 826, the decoder 224 decodes the lasttraded price type. Then, at step 828, the decoder 224 determines if thelast traded price type was decoded to be “Other.” If it was not, themethod continues at step 832. If it was, at step 830, the decoder 224decodes the last traded price change. Then, at step 832, the decoder 224decodes the last traded quantity, and the method 800 terminates.

It will be apparent to those of ordinary skill in the art that methodsinvolved in the system and method for encoding/decoding market datausing probability data may be embodied in a computer program productthat includes one or more computer readable media. For example, acomputer readable medium can include a readable memory device, such as ahard drive device, a CD-ROM, a DVD-ROM, or a computer diskette, havingcomputer readable program code segments stored thereon. The computerreadable medium can also include a communications or transmissionmedium, such as, a bus or a communication link, either optical, wired orwireless having program code segments carried thereon as digital oranalog data signals.

1-21. (canceled)
 22. A method for providing market data to a cliententity in an electronic trading environment, the method including:receiving by a computing device first market data in a first messagefrom an electronic exchange, wherein the first market data includes afirst price for a parameter for a tradeable object; storing by thecomputing device the first price; receiving by the computing devicesecond market data in a second message from the electronic exchange,wherein the second market data includes a second price for the tradeableobject, wherein the second price is for the same parameter as the firstprice; calculating by the computing device a difference, wherein thedifference is the change in value between the first price and the secondprice; encoding by the computing device the calculated difference,wherein the calculated difference is encoded by providing the calculateddifference to a statistical data encoder as an input symbol, wherein thestatistical data encoder provides an encoded difference representing thecalculated difference; generating by the computing device an updatemessage including the encoded difference; and sending by the computingdevice the update message to a client entity.
 23. The method of claim22, wherein the computing device is part of the electronic exchange. 24.The method of claim 22, wherein the computing device is a gateway. 25.The method of claim 22, wherein the first market data includes a firstquantity for the tradeable object, wherein the second market dataincludes a second quantity for the tradeable object, wherein the secondquantity is for the same parameter as the first quantity, and furtherincluding: storing by the computing device the first quantity;calculating by the computing device a quantity difference, wherein thequantity difference is the change in value between the first quantityand the second quantity; encoding by the computing device the calculatedquantity difference, wherein the calculated quantity difference isencoded by providing the calculated quantity difference to thestatistical data encoder as an input symbol, wherein the statisticaldata encoder provides an encoded quantity difference representing thecalculated quantity difference; and generating by the computing devicethe update message further including the encoded quantity difference.26. The method of claim 22, wherein the first price and the second priceare for a best bid parameter.
 27. The method of claim 22, wherein thefirst price and the second price are for a best ask parameter.
 28. Themethod of claim 22, wherein the first price and the second price are fora last traded price parameter.
 29. A computer readable medium havingstored therein instructions executable by a processor to perform amethod including: receiving by a computing device first market data in afirst message from an electronic exchange, wherein the first market dataincludes a first price for a parameter for a tradeable object; storingby the computing device the first price; receiving by the computingdevice second market data in a second message from the electronicexchange, wherein the second market data includes a second price for thetradeable object, wherein the second price is for the same parameter asthe first price; calculating by the computing device a difference,wherein the difference is the change in value between the first priceand the second price; encoding by the computing device the calculateddifference, wherein the calculated difference is encoded by providingthe calculated difference to a statistical data encoder as an inputsymbol, wherein the statistical data encoder provides an encodeddifference representing the calculated difference; generating by thecomputing device an update message including the encoded difference; andsending by the computing device the update message to a client entity.30. The computer readable medium of claim 29, wherein the computingdevice is part of the electronic exchange.
 31. The computer readablemedium of claim 29, wherein the computing device is a gateway.
 32. Thecomputer readable medium of claim 29, wherein the first market dataincludes a first quantity for the tradeable object, wherein the secondmarket data includes a second quantity for the tradeable object, whereinthe second quantity is for the same parameter as the first quantity, andfurther including instructions for: storing by the computing device thefirst quantity; calculating by the computing device a quantitydifference, wherein the quantity difference is the change in valuebetween the first quantity and the second quantity; encoding by thecomputing device the calculated quantity difference, wherein thecalculated quantity difference is encoded by providing the calculatedquantity difference to the statistical data encoder as an input symbol,wherein the statistical data encoder provides an encoded quantitydifference representing the calculated quantity difference; andgenerating by the computing device the update message further includingthe encoded quantity difference.
 33. The computer readable medium ofclaim 29, wherein the first price and the second price are for a bestbid parameter.
 34. The computer readable medium of claim 29, wherein thefirst price and the second price are for a best ask parameter.
 35. Thecomputer readable medium of claim 29, wherein the first price and thesecond price are for a last traded price parameter.
 36. A system forproviding market data to a client entity in an electronic tradingenvironment, the system including: a computing device adapted to receivefirst market data in a first message from an electronic exchange,wherein the first market data includes a first price for a parameter fora tradeable object, wherein the computing device is adapted to store thefirst price, wherein the computing device is adapted to receive secondmarket data in a second message from the electronic exchange, whereinthe second market data includes a second price for the tradeable object,wherein the second price is for the same parameter as the first price,wherein the computing device is adapted to calculate a difference,wherein the difference is the change in value between the first priceand the second price, wherein the computing device is adapted to encodethe calculated difference, wherein the calculated difference is encodedby providing the calculated difference to a statistical data encoder asan input symbol, wherein the statistical data encoder provides anencoded difference representing the calculated difference, wherein thecomputing device is adapted to generate an update message including theencoded difference, and wherein the computing device is adapted to sendthe update message to a client entity.
 37. The system of claim 36,wherein the computing device is part of the electronic exchange.
 38. Thesystem of claim 36, wherein the computing device is a gateway.
 39. Thesystem of claim 36, wherein the first market data includes a firstquantity for the tradeable object, wherein the second market dataincludes a second quantity for the tradeable object, wherein the secondquantity is for the same parameter as the first quantity, and whereinthe computing device is further adapted to store the first quantity,wherein the computing device is further adapted to calculate a quantitydifference, wherein the quantity difference is the change in valuebetween the first quantity and the second quantity, wherein thecomputing device is further adapted to encode the calculated quantitydifference, wherein the calculated quantity difference is encoded byproviding the calculated quantity difference to the statistical dataencoder as an input symbol, wherein the statistical data encoderprovides an encoded quantity difference representing the calculatedquantity difference, and wherein the computing device is further adaptedto generate the update message further including the encoded quantitydifference.
 40. The system of claim 36, wherein the first price and thesecond price are for a best bid parameter.
 41. The system of claim 36,wherein the first price and the second price are for a best askparameter.
 42. The system of claim 36, wherein the first price and thesecond price are for a last traded price parameter.